Showing papers on "De novo protein structure prediction published in 2006"

An Introduction to Modeling Structure from Sequence

Abstract: The underlying premise behind all attempts to determine a large number of diverse protein structures is that the total number of protein domain folds is much smaller, by many orders of magnitude, than the total number of sequences; in other words, many sequences adopt essentially the same fold. If the fold of a protein could be recognized from sequence information alone, then a complete database of all possible folds would allow the structure corresponding to any sequence to be modeled. The growth of structure determination has turned most biochemists and biologists into consumers of structural information. As the demand for such information continues to outstrip the supply, all aspects of structure modeling assume increasing importance. This unit provides an introduction to modeling structure from its sequence and surveys the currently available methods described in the subsequent units of this chapter. Keywords: Protein structure modeling; protein fold; 3-D structure

Presenting a Novel Neural Network Architecture for Membrane Protein Prediction

Abstract: In the early seventies, it was clear that primary amino acid sequence and its local solution environment hold most of the information necessary for protein folding. Since then, scientists have been trying to solve the bioinformatics problem by constructing the tertiary three-dimensional structure of protein from the primary amino acid sequences by using computational biology. Success of several genome sequencing projects put considerable momentum in an effort to analyze these bio-chemically uncharacterized sequence. A handful of methods are developed to solve the problem of globular proteins prediction because of the easy availability of the data but the prediction of membrane protein structures is a key area that remains mainly unsolved. The problem of prediction is made topologically more complex by the presence of several transmembrane domains in many proteins, and current tools are far away from achieving significant reliability in prediction. But from a pharma-economical perspective, though it is the fact that membrane proteins constitute ∼75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biomedical research. In this paper we present novel neural networks (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.